Publications

Abstract

Many patients experience financial hardship after acute injuries or emergency surgery. Financial hardship, often referred to as "financial toxicity," comprises material hardship due to unexpected bills and income loss, psychosocial stress due to uncertainty of one's financial future, and negative coping behaviors such as forgoing necessary care due to costs. These factors combine to have detrimental effects on physical and mental health and prevent optimal recovery after injury or illness. Although there is a growing appreciation that acute care surgeons should understand and evaluate financial hardship in their own populations to facilitate the optimal recovery of their patients, consensus is lacking on the best ways to measure financial hardship among the trauma and emergency surgery patient population. This current opinion will define the scope of the problem and provide pragmatic first steps to enable the evaluation of long-term, patient-level financial outcomes at your institution-including specific questions that encompass the five domains and the five mediators of financial hardship. This effort presents an updated theoretical framework, challenges common terminology, and synthesizes the most relevant tools for measuring financial hardship, presenting recommended screening questions that can be immediately used to assess long-term financial outcomes in trauma and emergency surgery populations, and standardizing data collection across institutions and facilitating larger-scale investigations.

View details for DOI 10.1136/tsaco-2025-001856

View details for PubMedID 41262857

View details for PubMedCentralID PMC12625838

Abstract

Optimal provider-to-patient (PtP) ratios in acute care surgery (ACS) remain undefined despite their importance for care quality and provider sustainability. This study aimed to understand surgeon perspectives on maximum ideal ratios across trauma, emergency general surgery (EGS) and surgical intensive care unit (SICU) services.This multicenter mixed-methods study combined quantitative surveys and semistructured interviews with ACS surgeons at level I/II trauma centers across the USA (1 August 2023-19 April 2024). Service line census data were also collected. Interviews were recorded, transcribed and qualitative analysis performed; surveys were analyzed with descriptive statistics.Fifty-two interviews were completed. Survey response rate was 50.3% (212/421 eligible division leadership and faculty) from 40 centers across 24 states. The perceived maximum safe patient load for trauma and EGS was <20 patients when working independently, and up to 40 patients with full team support. SICU ratios were lower with most reporting ≤10 patients for independent coverage and ≤20 with team support. Regarding appropriate patient loads for junior residents and advanced practice providers, most respondents recommended ≤10 patients for trauma/EGS and ≤7 for SICU. For senior residents, most recommended ≤13 patients for trauma/EGS and ≤7 for SICU. Notably, 72% of centers exceeded their own leadership-recommended maximums for at least one service line. Qualitative analysis revealed patient acuity, team experience and competing demands as key workload modulators, with concerns about care quality degradation and burnout at higher ratios.This study establishes potential upper threshold benchmarks for ACS PtP ratios with strong agreement across institutions. Division leadership should consider developing staffing models that account for patient acuity and service complexity while implementing escalation protocols for sustained high workloads. Current practices frequently exceed maximum ideal ratios, highlighting the need for evidence-based staffing guidelines that balance financial constraints with mounting evidence linking workload intensity and density to adverse outcomes.IV.

View details for DOI 10.1136/tsaco-2025-001937

View details for PubMedID 41262852

View details for PubMedCentralID PMC12625922

Abstract

BACKGROUND: Acute care surgery (ACS) involves rapid, high-stakes decisions with limited opportunity for preoperative planning. While machine learning (ML) may improve risk prediction and decision making in this setting, its development, validation, and implementation in ACS remain understudied. We therefore evaluated the techniques, predictor features, and outcomes used in ML-driven risk prediction models in ACS and generated recommendations to inform future research and support clinically meaningful implementation.METHODS: A systematic review of ML-driven predictive models in ACS (emergency general surgery, surgical critical care, trauma) was conducted. Models were analyzed by predictor features, outcomes, algorithms, and performance. The best-performing models for the most commonly predicted outcome were identified.RESULTS: Of 52 studies, 57.7% focused on trauma populations. Most models used registry data (76.8%), fewer used electronic health records (28.8%), and only five studies performed external validation after model development. Common algorithms included logistic regression (44.2%), random forest (34.6%), and decision trees (26.9%). Mortality (59.6%), complications (30.8%), and triage/severity (15.4%) were the most frequent outcomes; patient-centered/reported outcomes were absent. Features commonly included demographics, physiologic scores, and vital signs, while imaging and intraoperative data were underused. Natural language processing was used in four studies. Model performance was typically assessed using area under the receiver operating characteristic curve (88.5%), with support vector machines demonstrating the highest performance. Machine learning models generally outperformed conventional risk scores among 11 comparative studies.CONCLUSION: Machine learning-driven predictive models in ACS show promising performance but are constrained by limited methodological rigor, real-world validation, and substantial heterogeneity in features, outcomes, and algorithms, challenging systematic adoption and oversight. A grounded understanding of ACS decision making workflows and their postimplementation impact may ensure clinically relevant, seamless, and safe integration of ML-based risk prediction.LEVEL OF EVIDENCE: Systematic Review Without Meta-analysis; Level IV.

View details for DOI 10.1097/TA.0000000000004805

View details for PubMedID 41196221

Abstract

BACKGROUND: Acute care surgery (ACS) patients face financial burdens and impact on quality-of-life (QoL), which can be significant for the uninsured. Hospital Presumptive Eligibility (HPE) aims to reduce costs and improve access. We evaluated patient-reported outcomes following hospitalization, hypothesizing that HPE improved recovery trajectory.STUDY DESIGN: A convergent mixed methods study of ACS patients 18-64 years was performed at an academic Level I trauma center from December 2024 to August 2025. HPE patients were compared with insured patients. SF-12 QoL and American Association for the Surgery of Trauma (AAST) financial hardship survey tool were completed at hospitalization and 1-3 months post-discharge. Thematic analysis of semi-structured interviews was conducted to evaluate access to care and financial toxicity.RESULTS: Ten out of the 110 patients were HPE and the rest were insured controls. HPE patients were younger (median: 39.5 vs. 43.5 years), had higher ICU admission (30% vs. 9%) and non-routine discharge (22% vs.7%) rates. At 3 months post-discharge, both groups had reduced household income (HPE vs. controls: 33% vs. 20%) and difficulty paying non-medical bills (50% vs. 25%). HPE patients additionally reported lower SF-12 measures. In qualitative analysis, HPE patients cited rapid access to insurance and expected reduction in out-of-pocket cost as program benefits.CONCLUSION: Risk for financial toxicity remains high among ACS patients. Patients enrolled in HPE faced additional financial and psychosocial strains during post-admission recovery. QoL and financial metrics are important to understand longitudinal patient outcomes and guide policies to improve patient recovery.

View details for DOI 10.1097/XCS.0000000000001652

View details for PubMedID 41051099

Abstract

To evaluate ChatGPT's ability to perform thematic analysis of medical Best Practice Advisory (BPA) free-text comments and identify prompt engineering strategies that optimize performance.We analyzed 778 BPA comments from a pilot AI-enabled clinical deterioration intervention at Stanford Hospital, categorized as reasons for deterioration (Category 1) and care team actions (Category 2). Prompt engineering strategies (role, context specification, stepwise instructions, few-shot prompting, and dialogue-based calibration) were tested on a 20% random subsample to determine the best-performing prompt. Using that prompt, ChatGPT conducted deductive coding on the full dataset followed by inductive analysis. Agreement with human coding was assessed as inter-rater reliability (IRR) using Cohen's Kappa (κ).With structured prompts and calibration, ChatGPT achieved substantial agreement with human coding (κ = 0.76 for Category 1; κ = 0.78 for Category 2). Baseline agreement was higher for Category 1 than Category 2, reflecting differences in comment type and complexity, but calibration improved both. Inductive analysis yielded 9 themes, with ChatGPT-generated themes closely aligning with human coding.ChatGPT can accelerate qualitative analysis, but its rigor depends heavily on prompt engineering. Key strategies included role and context specification, pulse-check calibration, and safeguard techniques, which enhanced reliability and reproducibility.This study demonstrates the feasibility of ChatGPT-assisted thematic analysis and introduces a structured approach for applying LLMs to qualitative analysis of clinical free-text data, underscoring prompt engineering as a methodological lever.

View details for DOI 10.1093/jamiaopen/ooaf126

View details for PubMedID 41487277

View details for PubMedCentralID PMC12757007

View details for DOI 10.1093/jamiaopen/ooaf126

View details for Web of Science ID 001604247000001

View details for Web of Science ID 001551680300102

Abstract

Gastrointestinal complications requiring emergency general surgery (EGS) after cardiac surgery are associated with high morbidity and mortality. Identifying predictors of 30-d mortality and intuitive preoperative laboratory-based futility thresholds may enhance risk stratification and clinical decision-making.We conducted a single-center retrospective cohort study of adults aged 18-90 y who underwent cardiac surgery between 2013 and 2023. Patients requiring EGS intervention for gastrointestinal complications during their index cardiac surgery hospitalization were included. Exclusion criteria were intraoperative EGS consultations and consults without surgical intervention. Preoperative laboratory-based futility thresholds and independent predictors of 30-d mortality were identified using multivariable logistic regression.Ninety-five patients met inclusion criteria; 30-d mortality was 51%. Ischemic bowel was the most common diagnosis (56%). Nonsurvivors had higher rates of obesity (body mass index ≥ 30: 42% versus 17%, P = 0.008), elevated Sequential Organ Failure Assessment (SOFA) scores (median [interquartile range]: 13 [11-14] versus 10 [7-11], P < 0.001), and end-stage renal disease (60% versus 19%, P < 0.001). They also had higher lactate (7.5 versus 3.4 mmol/L, P < 0.001) and lower platelet counts (83 versus 134 × 103/μL, P < 0.001). Laboratory thresholds associated with 100% 30-d mortality included platelet count < 95 × 103/μL, white blood cell count < 4.3 × 103/μL, lactate > 3.4 mmol/L, and total bilirubin > 3.9 mg/dL. Independent predictors of mortality were SOFA ≥ 11.5 (adjusted odds ratios [aOR] 3.2, P = 0.04), body mass index ≥ 30 (aOR 4.0, P = 0.04), and platelets <100 × 103/μL (aOR 5.9, P = 0.02).Post-cardiac surgery patients involving EGS intervention have high 30-d mortality. Elevated SOFA scores, obesity, and thrombocytopenia are critical predictors, offering opportunities for improved risk stratification and targeted management strategies.

View details for DOI 10.1016/j.jss.2025.06.007

View details for PubMedID 40652731

Abstract

ObjectivesWhile concomitant vascular injury is associated with an increased risk of amputation following lower extremity trauma, risk factors for amputation after attempted revascularization are lesser known. In centers where dedicated vascular traumatic expertise is not available, a lack of guidance regarding high-risk vascular trauma may limit efforts to appropriately triage and transfer patients to a higher level of care. We identified factors associated with in-hospital amputation after revascularization for isolated lower extremity trauma.MethodsThe American College of Surgeons Trauma Quality Improvement Program (ACS TQIP) is a multicenter, prospectively maintained database containing deidentified traumatic admissions data for over 900 trauma centers in the United States. From 2017 to 2021, ACS TQIP was queried for adult patients undergoing arterial revascularization following isolated lower extremity trauma. Injury-related variables were derived from structured data fields, Injury Severity Scores, and Abbreviated Injury Scores. The primary endpoint was post-revascularization in-hospital lower extremity amputation. Univariate and multivariate logistic regression of demographic data, medical history, and injury-related variables were performed to identify factors associated with post-revascularization amputation.ResultsOf 5669 patients undergoing revascularization, 10.2% underwent amputation a median 8.31 days after their surgical procedure. Most revascularizations were done via open surgical approach (81.9%), followed by endovascular (13.8%) and hybrid (4.3%) methods. Amputated patients were older (39.5 vs 35.6 years, p < 0.001, Table 1) and more likely to have a preoperative history of peripheral arterial disease (1.4% vs 0.6%, p = 0.017). On multivariate logistic regression, blunt mechanism (OR 4.80, p < 0.001, Table 2), popliteal arterial injury (OR 2.11, p < 0.001), and concurrent bony injury (OR 2.03, p < 0.001) were independently associated with amputation.ConclusionsIn the multicenter American College of Surgeons Trauma Quality Improvement Program, the overall rate of post-revascularization amputation in patients with isolated lower extremity trauma was 10.20%. Amputation risk was higher in patients with advanced age and comorbidity, suggesting that triage for revascularization already incorporates an evaluation of patient frailty. In multivariate analysis, blunt mechanism of injury, popliteal artery injury, and bony injury were independently associated with amputation. Each additional hour between admission and revascularization was associated with greater amputation risk, highlighting the importance of efforts to expediently and appropriately triage patients at with high-risk injuries to optimize limb salvage outcomes.

View details for DOI 10.1177/17085381251360074

View details for PubMedID 40626327